Triple
T21532971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brazilian Midwest |
E531280
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Rondonópolis |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Rondonópolis | Statement: [Brazilian Midwest, containsCity, Rondonópolis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rondonópolis Context triple: [Brazilian Midwest, containsCity, Rondonópolis]
-
A.
Rondonópolis
chosen
Rondonópolis is a major agricultural and commercial city in the state of Mato Grosso in central-western Brazil.
-
B.
Dourados
Dourados is a major agricultural and commercial city in the Brazilian state of Mato Grosso do Sul, known as an important regional economic and educational center.
-
C.
Brasópolis
Brasópolis is a municipality in the state of Minas Gerais, Brazil, known for its mountainous landscapes and proximity to the Mantiqueira mountain range.
-
D.
Três Lagoas
Três Lagoas is a Brazilian city in the state of Mato Grosso do Sul known for its strong pulp and paper industry and growing industrial sector.
-
E.
Porto Velho
Porto Velho is the capital and largest city of the Brazilian state of Rondônia, located in the western Amazon region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d0ae0e88190a6042effd93cd455 |
completed | April 26, 2026, 11:17 p.m. |
Created at: April 16, 2026, 6:27 p.m.